Process:
1st) Simulate population dataset based on questions from recruitment form. This represents a rough guess at the total population of those living in 30 - 60% AMI in Boulder City. This fake dataset is based on initial estimates and/or guesses on demographic parameters (including what the parameters should be). This population dataset is just for the purposes of illustration.
2nd) Randomly sample 4000 applicants from the simulated population data.
3rd) Select first ‘wave’ of 200 program selections using two methods: A - a custom weighting procedure B - a purely random sample of 200 selections from the applicant pool
4th) Select second and third waves using propensity score matching against the applicant pool ((Ho et al. 2011))1.
5th) Make list of additional backups to use for additional verification if needed. Define a process for selecting these additional backup selections, based on prioritizing the least represented groups.
Last) Make the dataset with selections and backups available for download (see 6).
There will be enough recruits into the program that we can have multiple waves of selections within the weighting criteria we define.
Failures of verification will be ~randomly distributed across groups.
For the sake of the simulations and calculations here (which are just for an abstract presentation of the process), assume there will be 4000 applicants, 200 selections, and 200 backups in each of three sampling waves. We are also assuming that the applicant pool is a random selection from the population (which probably won’t be the case in our intended application).
For the purposes of weighting, assume groups are independent. That is, we have estimates for the proportion of the population by racial category and we use these weights to make a random selection, likewise with gender, and disability, etc.
Ideally, make all matches based on estimates of population in Boulder City who are either a) between 30 and 60 % of area median income (AMI) or b) below poverty line. Option a is preferable - b is backup if we encounter data limilations.
Proportionate match by race/ethnicty, gender identity, and disability status.
Individuals with children under 18 should be represented in the program at ~2xs their estimated representation in the population
*These options will change once we have closed in data for population estimates*
The eligibility questionnaire will have questions on each of the above, plus additional eligibility and other characteristics not addressed here.
Ethnicity/race options:
Non-Latino White (e.g., German, Irish, English, Italian)
Hispanic, Latinx, or Spanish origin (e.g., Mexican/Mexican American, Puerto Rican, Cuban, Dominican, Salvadoran, Colombian)
Black or African American (e.g., African American, Jamaican, Haitian, Nigerian, Ethiopian, Somalian)
Asian (e.g., Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese)
American Indian or Alaska Native (e.g., Navajo Nation, Blackfeet Tribe, Muscogee (Creek) Nation, Mayan, Doyon, Native Village of Barrow Inupiat Traditional Government)
Native Hawaiian or Other Pacific Islander (e.g., Native Hawaiian, Samoan, Guamanian or Chamorro, Tongan, Fijian, Marshallese)
Middle Eastern or North African (e.g., Lebanese, Egyptian)
Not Listed (please specify)
Gender:
Woman
Man
Transgender
Non-binary/Gender non-conforming
Prefer to self identify (please write in your preferred identity here)
Households with children under 18
calculated from general question on household composition, which includes a relationship and birthday question, which are in turn used to calculate if household has children under 18
assume this is a binary variable 1/0 for 1 = household with children under 18
Disability status:
This table shows the probabilities that we are working with in the current iteration of our fake data. These are a combination of empirical estimates and rough guesses (for now).
| sub_group | target_props |
|---|---|
| race_ethnicity | |
| White (not latino) | 0.756 |
| Hispanic | 0.100 |
| Black or African American | 0.014 |
| Asian | 0.051 |
| American Indian or Alaska Native | 0.002 |
| Native Hawaiian or Other Pacific Islander | 0.001 |
| Middle Eastern or North African | 0.038 |
| Not Listed | 0.038 |
| gender | |
| Woman | 0.398 |
| Man | 0.502 |
| Transgender | 0.030 |
| Non-binary/Gender non-conforming | 0.030 |
| Prefer to self identify | 0.040 |
| child_household | |
| No | 0.600 |
| Yes | 0.400 |
| disability | |
| None | 0.850 |
| Disability1 | 0.050 |
| Disability2 | 0.050 |
| Disability3 | 0.050 |
This table shows the sums across sub-groups as an initial internal check. They should generally sum to 1. The values for child household have already been manipulated to ensure twice as many households with children are included.
| group | group_sum |
|---|---|
| child_household | 1 |
| disability | 1 |
| gender | 1 |
| race_ethnicity | 1 |
Fake data for an arbitrary notion of the ‘total population’. This means all the people in Boulder living between 30 and 60% AMI. Right now this is 25000 people.
A few example rows from the simulated population sample:
| id | race_ethnicity | gender | child_household | disability |
|---|---|---|---|---|
| 18190 | White (not latino) | Woman | No | None |
| 18374 | White (not latino) | Woman | Yes | None |
| 1018 | White (not latino) | Woman | Yes | Disability2 |
| 3145 | White (not latino) | Woman | No | None |
| 23489 | White (not latino) | Man | Yes | None |
| 8901 | Asian | Non-binary/Gender non-conforming | Yes | Disability3 |
Randomly select 4000 from the population.
| sub_group | count | proportions | target_proportions |
|---|---|---|---|
| child_household | |||
| No | 2433 | 0.608 | 0.600 |
| Yes | 1567 | 0.392 | 0.400 |
| disability | |||
| Disability1 | 213 | 0.053 | 0.050 |
| Disability2 | 213 | 0.053 | 0.050 |
| Disability3 | 196 | 0.049 | 0.050 |
| None | 3378 | 0.845 | 0.850 |
| gender | |||
| Man | 1995 | 0.499 | 0.502 |
| Non-binary/Gender non-conforming | 133 | 0.033 | 0.030 |
| Prefer to self identify | 180 | 0.045 | 0.040 |
| Transgender | 123 | 0.031 | 0.030 |
| Woman | 1569 | 0.392 | 0.398 |
| race_ethnicity | |||
| American Indian or Alaska Native | 9 | 0.002 | 0.002 |
| Asian | 198 | 0.050 | 0.051 |
| Black or African American | 57 | 0.014 | 0.014 |
| Hispanic | 411 | 0.103 | 0.100 |
| Middle Eastern or North African | 137 | 0.034 | 0.038 |
| Native Hawaiian or Other Pacific Islander | 5 | 0.001 | 0.001 |
| Not Listed | 158 | 0.040 | 0.038 |
| White (not latino) | 3025 | 0.756 | 0.756 |
Note: as a reminder/clarifier, in the above table the ‘proportions’ column is what we observe when we select 4000 rows/individuals from our simulated population data. The target_proportions are the values used to simulate the population data. These values will generally be very similar because when you sample a large-ish population at random you will mostly tend to maintain the proportions of its characteristic parts. No weighting is applied at this step because we assume that those who apply to the program are something like a random sample of all those who could apply (the ‘population’).
To select the first sample wave of 200 individuals from our 4000 applicant pool we first take a weighted sample of the data using the target proportions in Table 5.4.
The weighting procedure:
calculate the expected number of individuals in a sample of 200 if they were in the sample at exactly their expected proportions.
For any individuals that have expected counts <= 3, add three to their expected count. This is another way of increasing proportionate representation of rare characteristics.
Take a random sample of 25% of the target sample size of 200 and reserve this for individuals with rare characteristics. These are defined by examining the applicant pool and simply counting the characteristics of all the people in the pool. The sample of 50 (25% of 200 of the rarest 50% of characteristics within each group are reserved for inclusion in the final selected sample.
The remaining 75% are chosen by a simple weighting from the enrollee pool.
Lastly, if any characteristics are present in the enrollee pool but still missing the selected sample, select one person at random with that characteristic and replace someone chosen at random with the most common set of characteritics.
The target proportions in Table 5.4 are based on characteristics of participants, so this first step in the sampling selects more than 200. We then select 200 people for the first sampling wave using the procedure just described.
| sub_group | props | target_counts | count_rand | proportions_rand | count_w | proportions_w |
|---|---|---|---|---|---|---|
| race_ethnicity | ||||||
| Native Hawaiian or Other Pacific Islander | 0.001 | 1 | NA | NA | 2 | 0.010 |
| American Indian or Alaska Native | 0.002 | 1 | NA | NA | 3 | 0.015 |
| Black or African American | 0.014 | 3 | 1 | 0.005 | 4 | 0.020 |
| Middle Eastern or North African | 0.038 | 8 | 8 | 0.040 | 14 | 0.070 |
| Not Listed | 0.038 | 8 | 3 | 0.015 | 12 | 0.060 |
| Asian | 0.051 | 10 | 13 | 0.065 | 13 | 0.065 |
| Hispanic | 0.100 | 20 | 18 | 0.090 | 18 | 0.090 |
| White (not latino) | 0.756 | 151 | 157 | 0.785 | 134 | 0.670 |
| gender | ||||||
| Transgender | 0.030 | 6 | 5 | 0.025 | 11 | 0.055 |
| Non-binary/Gender non-conforming | 0.030 | 6 | 5 | 0.025 | 11 | 0.055 |
| Prefer to self identify | 0.040 | 8 | 7 | 0.035 | 16 | 0.080 |
| Woman | 0.398 | 80 | 87 | 0.435 | 72 | 0.360 |
| Man | 0.502 | 100 | 96 | 0.480 | 90 | 0.450 |
| disability | ||||||
| Disability2 | 0.050 | 10 | 8 | 0.040 | 9 | 0.045 |
| Disability3 | 0.050 | 10 | 13 | 0.065 | 18 | 0.090 |
| Disability1 | 0.050 | 10 | 11 | 0.055 | 11 | 0.055 |
| None | 0.850 | 170 | 168 | 0.840 | 162 | 0.810 |
| child_household | ||||||
| Yes | 0.400 | 80 | 72 | 0.360 | 77 | 0.385 |
| No | 0.600 | 120 | 128 | 0.640 | 123 | 0.615 |
The second wave selection works by taking the wave 1 selection and then using an algorithm to find each individuals closest match from the 3800 individuals remaining in the applicant pool. This is done using a technique called propensity score matching (Ho et al. 2011).
The third wave of sampled individuals is done with the same process.
First, lets compare the population data to the applicant data:
| sub_group | target_props | target_counts | count_w1 | props_w1 | count_w2 | props_w2 | count_w3 | props_w3 |
|---|---|---|---|---|---|---|---|---|
| race_ethnicity | ||||||||
| Native Hawaiian or Other Pacific Islander | 0.001 | 1 | 2 | 0.010 | 2 | 0.010 | 1 | 0.005 |
| American Indian or Alaska Native | 0.002 | 1 | 3 | 0.015 | 3 | 0.015 | 3 | 0.015 |
| Black or African American | 0.014 | 3 | 4 | 0.020 | 4 | 0.020 | 6 | 0.030 |
| Middle Eastern or North African | 0.038 | 8 | 14 | 0.070 | 12 | 0.060 | 11 | 0.055 |
| Not Listed | 0.038 | 8 | 12 | 0.060 | 13 | 0.065 | 15 | 0.075 |
| Asian | 0.051 | 10 | 13 | 0.065 | 13 | 0.065 | 10 | 0.050 |
| Hispanic | 0.100 | 20 | 18 | 0.090 | 18 | 0.090 | 18 | 0.090 |
| White (not latino) | 0.756 | 151 | 134 | 0.670 | 135 | 0.675 | 136 | 0.680 |
| gender | ||||||||
| Transgender | 0.030 | 6 | 11 | 0.055 | 14 | 0.070 | 14 | 0.070 |
| Non-binary/Gender non-conforming | 0.030 | 6 | 11 | 0.055 | 9 | 0.045 | 9 | 0.045 |
| Prefer to self identify | 0.040 | 8 | 16 | 0.080 | 17 | 0.085 | 14 | 0.070 |
| Woman | 0.398 | 80 | 72 | 0.360 | 71 | 0.355 | 70 | 0.350 |
| Man | 0.502 | 100 | 90 | 0.450 | 89 | 0.445 | 93 | 0.465 |
| disability | ||||||||
| Disability2 | 0.050 | 10 | 9 | 0.045 | 7 | 0.035 | 6 | 0.030 |
| Disability3 | 0.050 | 10 | 18 | 0.090 | 16 | 0.080 | 20 | 0.100 |
| Disability1 | 0.050 | 10 | 11 | 0.055 | 12 | 0.060 | 9 | 0.045 |
| None | 0.850 | 170 | 162 | 0.810 | 165 | 0.825 | 165 | 0.825 |
| child_household | ||||||||
| Yes | 0.400 | 80 | 77 | 0.385 | 75 | 0.375 | 78 | 0.390 |
| No | 0.600 | 120 | 123 | 0.615 | 125 | 0.625 | 122 | 0.610 |
Figure 5.1: Proportions by race group in simulated population data.
Figure 5.2: Proportions by gender in simulated population data.
We can examine just the race and gender breakdowns, above, to see that randomly sampling 4000 individuals from our population of 25000 leads to proportions in each group that are fairly similar.
Next, we can see how the proportions in each sampling wave compare to the ‘target’ proportions in the population data:
Figure 5.3: Proportions by racial grouping, sampling waves.
Figure 5.4: Proportions by gender, sampling waves.
Figure 5.5: Proportions of households with a child in the home, by sampling wave.
Figure 5.6: Proportions by disability status, sampling wave.
The first example dataset presents a column for each sampling wave. The intended use is that all the individuals in the far left column, Wave 1, are selected to the program for verification. If some of these individuals cannot be verified, their replacement is the cell in the same row immediately to the right, in the Wave 2 column. If someone in Wave 3 cannot be verified, then proceed to Wave 3.
Propensity score matching is a technique often used in quasi-experimental designs for statistically matching members of a treatment group to members of a control group. In our case, we use the same kind of algorithm to match each participant in sampling waves 2 and 3 with their most similar counter part in the applicant pool.↩︎